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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Fairness in Language Models

Fairness in Language Models

Zhang, Wenbin

Hardcover

Series: Artificial Intelligence: Foundations, Theory, and Algorithms

LinguisticsGeneral ComputersProbability & Statistics

PREORDER - Expected ship date January 18, 2027

ISBN10: 3032391458
ISBN13: 9783032391452
Publisher: Springer
Published: Jan 18 2027
Pages: 116
Language: English
As language models increasingly influence critical decisions in healthcare, hiring, and criminal justice, their capacity to perpetuate and amplify societal biases poses significant risks to marginalized communities. Although awareness of these fairness issues is growing, practitioners still face many barriers. The proliferation of competing fairness definitions leads to conceptual confusion and the lack of systematic guidance on how to select appropriate evaluation methods and mitigation strategies, whereas bias metrics are scattered across disconnected sources. This lack of structure has hindered progress in building fair and trustworthy language models. Motivated by these challenges, this book provides the first systematic, architecture-aware guide to bias in modern language models, offering a unified framework that synthesizes theory, measurement, and practical solutions. Covering models from BERT to GPT and beyond, the book gives readers the tools they need to understand and address bias effectively.

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